This series of files compile analyses done for the specific analysis of Chapter 1, for the local campaign of 2014.
All analyses have been done with PRIMER-e 6 and R 3.6.3.
Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it
We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.
Selected variables for the analyses:
- Percentage of organic matter: om
- Percentage of gravel: gravel
- Percentage of sand: sand
- Percentage of silt: silt
- Percentage of clay: clay
- Concentration of arsenic: arsenic
- Concentration of cadmium: cadmium
- Concentration of chromium: chromium
- Concentration of copper: copper
- Concentration of iron: iron
- Concentration of manganese: manganese
- Concentration of mercury: mercury
- Concentration of lead: lead
- Concentration of zinc: zinc
- Specific richness: S
- Total density of individuals: N
- Shannon’s diversity: H
- Piélou’s evenness: J
Abundances of Bipalponephtys neotena (Bneo) and Spisula solidissima (Ssol) were also considered (see IndVal and SIMPER results).
1. Data manipulation
For the following analyses, independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices. Variables have been standardized by mean and standard-deviation.
1.1. Identification of outliers
To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.
We identified stations 1 and 29 as general outliers. They have been deleted for the following analyses.

1.2. Correlations between parameters
Correlations have been calculated with Spearman’s rank coefficient.
According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions:
- cadmium, chromium and manganese concentrations (cadmium and manganese deleted)
- lead and zinc concentrations (zinc deleted)
We also decided to exclude clay content in the regressions, as it tends to increase drasticaly VIFs due to a marginal negative correlation with sand (very high \(R^{2}\)).
Correlation coefficients between habitat parameters and metals concentrations
| om |
1 |
-0.562 |
-0.118 |
-0.361 |
0.545 |
0.565 |
0.324 |
0.206 |
0.786 |
-0.123 |
0.363 |
0.701 |
0.656 |
0.676 |
| gravel |
-0.562 |
1 |
0.243 |
0.344 |
-0.752 |
-0.417 |
-0.211 |
-0.134 |
-0.498 |
-0.046 |
-0.379 |
-0.516 |
-0.506 |
-0.544 |
| sand |
-0.118 |
0.243 |
1 |
-0.616 |
-0.66 |
-0.327 |
-0.478 |
-0.554 |
-0.405 |
-0.543 |
-0.506 |
-0.287 |
-0.418 |
-0.464 |
| silt |
-0.361 |
0.344 |
-0.616 |
1 |
-0.138 |
-0.13 |
0.284 |
0.394 |
-0.111 |
0.328 |
0.077 |
-0.19 |
-0.045 |
-0.018 |
| clay |
0.545 |
-0.752 |
-0.66 |
-0.138 |
1 |
0.577 |
0.406 |
0.381 |
0.629 |
0.368 |
0.598 |
0.606 |
0.638 |
0.663 |
| arsenic |
0.565 |
-0.417 |
-0.327 |
-0.13 |
0.577 |
1 |
0.466 |
0.403 |
0.672 |
0.279 |
0.571 |
0.581 |
0.654 |
0.589 |
| cadmium |
0.324 |
-0.211 |
-0.478 |
0.284 |
0.406 |
0.466 |
1 |
0.865 |
0.528 |
0.6 |
0.796 |
0.462 |
0.808 |
0.792 |
| chromium |
0.206 |
-0.134 |
-0.554 |
0.394 |
0.381 |
0.403 |
0.865 |
1 |
0.463 |
0.766 |
0.798 |
0.456 |
0.761 |
0.739 |
| copper |
0.786 |
-0.498 |
-0.405 |
-0.111 |
0.629 |
0.672 |
0.528 |
0.463 |
1 |
0.234 |
0.577 |
0.648 |
0.725 |
0.832 |
| iron |
-0.123 |
-0.046 |
-0.543 |
0.328 |
0.368 |
0.279 |
0.6 |
0.766 |
0.234 |
1 |
0.68 |
0.136 |
0.459 |
0.446 |
| manganese |
0.363 |
-0.379 |
-0.506 |
0.077 |
0.598 |
0.571 |
0.796 |
0.798 |
0.577 |
0.68 |
1 |
0.591 |
0.798 |
0.757 |
| mercury |
0.701 |
-0.516 |
-0.287 |
-0.19 |
0.606 |
0.581 |
0.462 |
0.456 |
0.648 |
0.136 |
0.591 |
1 |
0.726 |
0.661 |
| lead |
0.656 |
-0.506 |
-0.418 |
-0.045 |
0.638 |
0.654 |
0.808 |
0.761 |
0.725 |
0.459 |
0.798 |
0.726 |
1 |
0.921 |
| zinc |
0.676 |
-0.544 |
-0.464 |
-0.018 |
0.663 |
0.589 |
0.792 |
0.739 |
0.832 |
0.446 |
0.757 |
0.661 |
0.921 |
1 |


2. Permutational Analyses of Variance
Results of univariate PermANOVAs on parameters and multivariate PermANOVA on the whole benthic community are presented in the table below. Variables have been standardized by mean and standard-deviation, and taxon densities were (log+1) transformed.
| om |
S |
S |
{HI1 HI2 HI3}, {HI4 R2}, {R1 R2 R3} |
| gravel |
S |
|
{HI1 HI2 HI3 HI4 R3 R4}, {R1 R2} |
| sand |
|
S |
All sites in the same group |
| silt |
S |
|
{HI1 HI2 HI3 HI4 R2 R3}, {R1 R2}, {R1 R4}, {R2 R3 R4} |
| clay |
|
S |
{HI1 HI2 HI3 HI4}, {HI4 R1 R2 R3 R4}, {R1 R2 R3}, {R3 R4} |
| arsenic |
|
S |
{HI1 HI2}, {HI3 HI4 R2}, {HI3 HI4 R1 R3 R4} |
| cadmium |
|
S |
All except {HI1 R2}, {HI1 R3}, {HI2 R2}, {HI2 R3}, {HI3 R2}, {HI3 R3} |
| chromium |
|
S |
{HI1 HI2 HI3 R1 R4}, {HI4 R2 R3 R4} |
| copper |
S |
S |
{HI1 HI2 HI3}, {HI1 HI3 HI4}, {HI4 R1 R2}, {R1 R2 R3}, {R2 R3 R4} |
| iron |
|
|
All except {HI1 R3}, {HI2 R3}, {R1 R3} |
| manganese |
|
S |
{HI1 HI2}, {HI3 HI4 R1 R4}, {R2 R3} |
| mercury |
|
|
{HI1 HI2 HI3}, {HI2 HI4 R1 R2 R3 R4} |
| lead |
|
S |
{HI1 HI2}, {HI1 HI3}, {HI4 R1 R2 R3 R4} |
| zinc |
S |
|
{HI1 HI2 HI3 HI4}, {HI4 R1 R2 R4}, {HI4 R2 R3 R4} |
| S (500 µm) |
|
S |
{HI1 HI2 HI3}, {HI4 R1 R3 R4}, {HI4 R2 R3 R4} |
| N (500 µm) |
|
S |
{HI1 HI2 HI3}, {HI4 R2 R3 R4}, {R1 R4} |
| H (500 µm) |
|
|
All except {HI2 HI3}, {HI3 HI4} |
| J (500 µm) |
|
S |
All except {HI1 HI4}, {HI1 R1}, {HI2 HI3}, {HI2 HI4}, {HI2 R1}, {HI2 R2} |
| ALL SPECIES (500 µm) |
S |
S |
{HI1 HI2}, {R1 R4}, {R2 R3} |
3. Similarity and characteristic species
Let’s have a look at the \(\beta\) diversity within our conditions and sites.
Results of the PERMDISP routine are shown below (mean and SE of the deviation from centroid for each group, i.e. multivariate dispersion), along with the mean Bray-Curtis dissimilarity for each group. Taxon densities were (log+1) transformed and PRIMER was used to do the PERMDISP.
Mean within-group Bray-Curtis dissimilarity for each condition or site
| HI |
37.2 |
3.79 |
0.544 |
| R |
49.7 |
1.81 |
0.72 |
| P1 |
22.6 |
2.47 |
0.359 |
| P2 |
21.7 |
0.28 |
0.343 |
| P3 |
18.9 |
2.73 |
0.302 |
| P4 |
48.3 |
3.39 |
0.764 |
| R1 |
44.9 |
3.85 |
0.711 |
| R2 |
40 |
2.2 |
0.631 |
| R3 |
41.2 |
6.52 |
0.657 |
| R4 |
42.5 |
4.21 |
0.671 |
Significative differences in dispersion have been detected between HI and R (p = 0.017), and between {HI1 HI2 HI3} and {HI4 R1 R2 R3 R4} by the PERMDISP and the pairwise tests.
The following analyses allowed to detect species as characteristic of each condition. We used results from PRIMER to justify further their choice.
## cluster indicator_value probability
## bipalponephtys_neotena 1 0.9035 0.001
## prionospio_steenstrupi 1 0.8660 0.001
## nephtys_sp 1 0.8260 0.001
## phoronida 1 0.7816 0.001
## phyllodoce_groenlandica 1 0.7764 0.001
## capitella_sp 1 0.7592 0.001
## cirratulidae_spp 1 0.7368 0.001
## limecola_balthica 1 0.7354 0.001
## sarsicytheridea_sp 1 0.6868 0.001
## polychaeta 1 0.6750 0.001
## scoloplos_armiger 1 0.6743 0.001
## eteone_sp 1 0.6242 0.001
## hediste_diversicolor 1 0.5500 0.001
## euchone_analis 1 0.4500 0.005
## pontoporeia_femorata 1 0.3500 0.005
## pholoe_sp 1 0.3474 0.029
## podocopida 1 0.3346 0.017
## diastylis_sculpta 1 0.3316 0.015
## glycera_dibranchiata 1 0.3275 0.008
## axinopsida_orbiculata 1 0.3000 0.022
## praxillella_praetermissa 1 0.3000 0.018
## sabellidae_spp 1 0.3000 0.025
## tharyx_sp 1 0.3000 0.019
## maldanidae_spp 1 0.2500 0.046
## spisula_solidissima 2 0.7181 0.001
## echinarachnius_parma 2 0.7000 0.001
## polygordius_sp 2 0.6005 0.003
## annelida 2 0.4992 0.003
## cancer_irroratus 2 0.2725 0.045
## halacaridae_spp 2 0.2500 0.039
##
## Sum of probabilities = 98.116
##
## Sum of Indicator Values = 27.96
##
## Sum of Significant Indicator Values = 16.23
##
## Number of Significant Indicators = 30
##
## Significant Indicator Distribution
##
## 1 2
## 24 6
SIMPER results (mean between-group Bray-Curtis dissimilarity: 0.858)
| bipalponephtys_neotena |
0.0603 |
0.0234 |
2.58 |
5.11 |
0.263 |
0.0703 |
| nephtys_sp |
0.0562 |
0.0274 |
2.05 |
4.77 |
0.139 |
0.136 |
| prionospio_steenstrupi |
0.0441 |
0.0179 |
2.46 |
3.53 |
0.139 |
0.187 |
| phoronida |
0.0346 |
0.0203 |
1.7 |
2.94 |
0.0693 |
0.227 |
| scoloplos_armiger |
0.0341 |
0.0201 |
1.7 |
3.02 |
0.562 |
0.267 |
| phyllodoce_groenlandica |
0.0311 |
0.0147 |
2.12 |
2.68 |
0.254 |
0.303 |
| capitella_sp |
0.0298 |
0.0169 |
1.76 |
2.58 |
0.139 |
0.338 |
| spisula_solidissima |
0.0294 |
0.0257 |
1.14 |
0.235 |
2.06 |
0.372 |
| phoxocephalus_holbolli |
0.0229 |
0.0228 |
1 |
1.08 |
1.61 |
0.399 |
| cirratulidae_spp |
0.0228 |
0.0152 |
1.5 |
1.94 |
0.0347 |
0.426 |
| limecola_balthica |
0.0226 |
0.0159 |
1.42 |
1.75 |
0.0347 |
0.452 |
| harpacticoida |
0.0219 |
0.0189 |
1.16 |
1.94 |
1.31 |
0.477 |
| sarsicytheridea_sp |
0.0207 |
0.0156 |
1.33 |
1.81 |
0.0347 |
0.502 |
| echinarachnius_parma |
0.0201 |
0.0203 |
0.995 |
0 |
1.4 |
0.525 |
| eteone_sp |
0.0161 |
0.0131 |
1.23 |
1.33 |
0.0549 |
0.544 |
| pholoe_minuta_tecta |
0.0137 |
0.0176 |
0.781 |
0.883 |
0.302 |
0.56 |
| polygordius_sp |
0.0137 |
0.0175 |
0.78 |
0.245 |
0.985 |
0.576 |
| hediste_diversicolor |
0.0135 |
0.0224 |
0.602 |
0.861 |
0 |
0.592 |
| euchone_analis |
0.0126 |
0.016 |
0.79 |
1.14 |
0 |
0.606 |
| pholoe_longa |
0.0123 |
0.0134 |
0.916 |
0.972 |
0.239 |
0.621 |
| pholoe_sp |
0.012 |
0.0138 |
0.866 |
0.954 |
0.145 |
0.635 |
| oligochaeta |
0.0101 |
0.0258 |
0.389 |
0.278 |
0.343 |
0.646 |
| mytilus_sp |
0.00938 |
0.0178 |
0.528 |
0.135 |
0.605 |
0.657 |
| annelida |
0.00902 |
0.0123 |
0.736 |
0.0693 |
0.681 |
0.668 |
| podocopida |
0.00875 |
0.0137 |
0.637 |
0.755 |
0.0347 |
0.678 |
| glycera_sp |
0.00863 |
0.0199 |
0.435 |
0.352 |
0 |
0.688 |
| pseudoleptocuma_minus |
0.00852 |
0.0124 |
0.686 |
0.205 |
0.42 |
0.698 |
| sabellidae_spp |
0.00822 |
0.0137 |
0.598 |
0.727 |
0 |
0.707 |
| pontoporeia_femorata |
0.00794 |
0.0119 |
0.667 |
0.643 |
0 |
0.717 |
| microphthalmus_sczelkowii |
0.00775 |
0.013 |
0.596 |
0.609 |
0.0896 |
0.726 |
| diastylis_sculpta |
0.00738 |
0.0111 |
0.668 |
0.626 |
0.0347 |
0.734 |
| spio_filicornis |
0.00706 |
0.0111 |
0.637 |
0.355 |
0.139 |
0.743 |
| aricidea_sp |
0.00699 |
0.0117 |
0.6 |
0.554 |
0.0896 |
0.751 |
| tharyx_sp |
0.00686 |
0.0113 |
0.609 |
0.534 |
0 |
0.759 |
| polychaeta |
0.00675 |
0.00638 |
1.06 |
0.624 |
0.208 |
0.767 |
| nephtys_caeca |
0.00653 |
0.00968 |
0.674 |
0.199 |
0.283 |
0.774 |
| glycera_dibranchiata |
0.00636 |
0.00864 |
0.737 |
0.504 |
0.0347 |
0.782 |
| solenoidea |
0.00627 |
0.00958 |
0.655 |
0.425 |
0.139 |
0.789 |
| praxillella_praetermissa |
0.00618 |
0.00979 |
0.631 |
0.545 |
0 |
0.796 |
| axinopsida_orbiculata |
0.00615 |
0.0102 |
0.6 |
0.542 |
0 |
0.803 |
| bivalvia |
0.00597 |
0.0093 |
0.642 |
0.351 |
0.167 |
0.81 |
| hemicythere_villosa |
0.00579 |
0.0106 |
0.547 |
0.339 |
0.199 |
0.817 |
| spiophanes_bombyx |
0.00564 |
0.0122 |
0.461 |
0.104 |
0.219 |
0.824 |
| halacaridae_spp |
0.00549 |
0.012 |
0.458 |
0 |
0.414 |
0.83 |
| phyllodoce_sp |
0.00509 |
0.0117 |
0.435 |
0.145 |
0.194 |
0.836 |
| cancer_irroratus |
0.00482 |
0.00769 |
0.626 |
0.0805 |
0.283 |
0.841 |
| eucratea_loricata |
0.00478 |
0.00676 |
0.707 |
0.243 |
0.173 |
0.847 |
| sertulariidae_spp |
0.00474 |
0.00678 |
0.7 |
0.555 |
0.451 |
0.853 |
| microphthalmus_sp |
0.0047 |
0.00968 |
0.486 |
0.42 |
0 |
0.858 |
| caprella_septentrionalis |
0.00439 |
0.0148 |
0.296 |
0 |
0.314 |
0.863 |
| edotia_triloba |
0.00423 |
0.00765 |
0.553 |
0.115 |
0.214 |
0.868 |
| psammonyx_nobilis |
0.00408 |
0.00939 |
0.434 |
0.0693 |
0.159 |
0.873 |
| maldanidae_spp |
0.00376 |
0.00688 |
0.546 |
0.305 |
0 |
0.877 |
| aricidea_acmira_catherinae |
0.00337 |
0.00911 |
0.37 |
0.0973 |
0.145 |
0.881 |
| cylichna_alba |
0.00297 |
0.00717 |
0.415 |
0.271 |
0 |
0.885 |
| capitellidae_spp |
0.00288 |
0.00778 |
0.37 |
0.19 |
0.0347 |
0.888 |
| brachyura |
0.00257 |
0.00623 |
0.413 |
0.196 |
0 |
0.891 |
| obelia_sp |
0.00256 |
0.00542 |
0.473 |
0.0347 |
0.139 |
0.894 |
| spionidae_spp |
0.00256 |
0.00506 |
0.506 |
0.0549 |
0.159 |
0.897 |
| campanulariidae_spp |
0.0025 |
0.0053 |
0.472 |
0.658 |
0.555 |
0.9 |
4. Univariate regressions
We used linear models for the all regressions on diversity indices. Outliers and correlated variables were removed from these analyses. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models).
4.1. Simple regressions
These analyses have been done to explore the relationships between variables. As it is a huge number of results to interpret, only multiple regressions will be included in the article.
Adjusted R-squared of simple regressions with all variables
| S |
0.2614 |
0.07195 |
-0.02463 |
0.2452 |
0.2742 |
-0.02566 |
0.3224 |
-0.009631 |
0.2168 |
0.3111 |
| N |
0.4527 |
0.1632 |
0.03359 |
0.2031 |
0.5568 |
0.1578 |
0.6478 |
-0.02778 |
0.2666 |
0.7254 |
| H |
-0.02759 |
-0.02024 |
-0.02717 |
-0.02656 |
0.01357 |
0.09775 |
-0.02313 |
-0.02743 |
-0.02778 |
0.02136 |
| J |
0.07927 |
0.04049 |
-0.02664 |
0.05976 |
0.2216 |
0.06225 |
0.1316 |
-0.01732 |
0.04197 |
0.2407 |
p-values of simple regressions with all variables
| S |
0.0006134 |
0.05695 |
0.7414 |
0.00093 |
0.0004393 |
0.7865 |
0.0001196 |
0.4264 |
0.001894 |
0.0001634 |
| N |
2.223e-06 |
0.006893 |
0.1393 |
0.002651 |
4.565e-08 |
0.007834 |
6.818e-10 |
0.993 |
0.0005358 |
7.372e-12 |
| H |
0.935 |
0.6092 |
0.8847 |
0.8374 |
0.2273 |
0.0315 |
0.6884 |
0.9123 |
0.9951 |
0.1872 |
| J |
0.04813 |
0.1182 |
0.843 |
0.07542 |
0.00168 |
0.0712 |
0.01443 |
0.5469 |
0.1142 |
0.001041 |
4.2. Multiple regressions
This section presents analyses done to determine (i) which model (metals, parameters or all) decribes the best the parameters and (ii) which variables are the most important to explain the parameters.
4.2.1. Best model selection
The aim here is to know which model is the best to explain our data.
Richness
| Full model |
38 |
12 |
97.27 |
6.963 |
0.44 |
| Parameters |
38 |
6 |
99.41 |
9.101 |
0.34 |
| Metals |
38 |
8 |
90.3 |
0 |
0.5 |
Density
| Full model |
38 |
12 |
60.4 |
0 |
0.78 |
| Parameters |
38 |
6 |
84.97 |
24.57 |
0.54 |
| Metals |
38 |
8 |
60.82 |
0.4177 |
0.77 |
Diversity
| Full model |
38 |
12 |
118.5 |
5.511 |
0.05 |
| Parameters |
38 |
6 |
119.5 |
6.562 |
-0.09 |
| Metals |
38 |
8 |
113 |
0 |
0.12 |
Evenness
| Full model |
38 |
12 |
112.2 |
5.187 |
0.18 |
| Parameters |
38 |
6 |
113.4 |
6.413 |
0.05 |
| Metals |
38 |
8 |
107 |
0 |
0.23 |
4.2.2. Significative variables selection
We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the tables below:
- for the model with all variables
| om |
|
+ |
|
|
| gravel |
|
|
+ |
|
| sand/clay |
|
|
- |
- |
| silt |
|
- |
|
|
| arsenic |
|
+ |
- |
- |
| chromium/cadmium/manganese |
- |
+ |
- |
- |
| copper |
|
+ |
|
|
| iron |
|
- |
+ |
+ |
| mercury |
+ |
|
|
|
| lead/zinc |
+ |
|
+ |
|
| Adjusted \(R^{2}\) |
0.55 |
0.79 |
0.17 |
0.28 |
- for the model with habitat parameters
| om |
+ |
+ |
|
- |
| gravel |
|
|
|
|
| sand/clay |
|
- |
|
|
| silt |
- |
- |
|
|
| Adjusted \(R^{2}\) |
0.36 |
0.55 |
0 |
0.08 |
- for the model with heavy metals
| arsenic |
- |
|
|
|
| chromium/cadmium/manganese |
- |
- |
- |
|
| copper |
+ |
|
|
|
| iron |
+ |
|
+ |
+ |
| mercury |
+ |
+ |
|
|
| lead/zinc |
+ |
+ |
|
- |
| Adjusted \(R^{2}\) |
0.55 |
0.78 |
0.14 |
0.27 |
Details of the regressions, with diagnostics and cross-validation, are summarized below.
All variables
Richness
## FULL MODEL
## Adjusted R2 is: 0.44
Fitting linear model: S ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
| (Intercept) |
0.2146 |
0.2632 |
0.815 |
0.4222 |
|
| om |
-0.1245 |
0.2954 |
-0.4215 |
0.6767 |
|
| gravel |
0.7738 |
1.399 |
0.5529 |
0.5849 |
|
| sand |
-0.1234 |
0.2005 |
-0.6158 |
0.5432 |
|
| silt |
-0.1265 |
0.2078 |
-0.6086 |
0.5479 |
|
| arsenic |
-0.06305 |
0.3048 |
-0.2068 |
0.8377 |
|
| chromium |
-0.6247 |
0.3274 |
-1.908 |
0.06702 |
|
| copper |
-0.0802 |
0.3662 |
-0.219 |
0.8283 |
|
| iron |
-0.02247 |
0.1681 |
-0.1337 |
0.8946 |
|
| mercury |
0.6446 |
0.5486 |
1.175 |
0.2502 |
|
| lead |
1.026 |
0.6953 |
1.475 |
0.1517 |
|
## RMSE from cross-validation: 1.196093
Variance Inflation Factors
| VIF |
2.43 |
1.47 |
1.66 |
1.7 |
2.47 |
2.69 |
2.97 |
1.39 |
2.05 |
5.61 |

## REDUCED MODEL
## Adjusted R2 is: 0.55
Fitting linear model: S ~ chromium + mercury + lead
| (Intercept) |
0.06598 |
0.116 |
0.569 |
0.5731 |
|
| chromium |
-0.5891 |
0.1501 |
-3.926 |
0.0004006 |
* * * |
| mercury |
0.4833 |
0.2745 |
1.761 |
0.08726 |
|
| lead |
0.8854 |
0.1679 |
5.274 |
7.578e-06 |
* * * |
## RMSE from cross-validation: 1.056735
Variance Inflation Factors
| VIF |
1.37 |
1.13 |
1.5 |

Density
## FULL MODEL
## Adjusted R2 is: 0.78
Fitting linear model: N ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
| (Intercept) |
-0.1107 |
0.1621 |
-0.6829 |
0.5005 |
|
| om |
0.3911 |
0.1818 |
2.151 |
0.04061 |
* |
| gravel |
-0.5637 |
0.8615 |
-0.6543 |
0.5185 |
|
| sand |
-0.01695 |
0.1234 |
-0.1374 |
0.8918 |
|
| silt |
-0.1169 |
0.1279 |
-0.9143 |
0.3687 |
|
| arsenic |
0.2955 |
0.1877 |
1.575 |
0.127 |
|
| chromium |
0.1019 |
0.2015 |
0.5057 |
0.6172 |
|
| copper |
0.1279 |
0.2254 |
0.5676 |
0.575 |
|
| iron |
-0.1257 |
0.1035 |
-1.214 |
0.2352 |
|
| mercury |
-0.2897 |
0.3377 |
-0.8577 |
0.3986 |
|
| lead |
0.2056 |
0.4281 |
0.4804 |
0.6348 |
|
## RMSE from cross-validation: 0.750523
Variance Inflation Factors
| VIF |
2.43 |
1.47 |
1.66 |
1.7 |
2.47 |
2.69 |
2.97 |
1.39 |
2.05 |
5.61 |

## REDUCED MODEL
## Adjusted R2 is: 0.79
Fitting linear model: N ~ om + silt + arsenic + chromium + copper + iron
| (Intercept) |
0.01572 |
0.07352 |
0.2139 |
0.832 |
|
| om |
0.3026 |
0.09456 |
3.2 |
0.003167 |
* * |
| silt |
-0.1786 |
0.08945 |
-1.997 |
0.05468 |
|
| arsenic |
0.3646 |
0.1159 |
3.145 |
0.00365 |
* * |
| chromium |
0.1814 |
0.1123 |
1.614 |
0.1166 |
|
| copper |
0.2154 |
0.1369 |
1.574 |
0.1257 |
|
| iron |
-0.1261 |
0.09095 |
-1.386 |
0.1755 |
|
## RMSE from cross-validation: 0.690898
Variance Inflation Factors
| VIF |
1.3 |
1.22 |
1.56 |
1.54 |
1.85 |
1.25 |

Diversity
## FULL MODEL
## Adjusted R2 is: 0.05
Fitting linear model: H ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
| (Intercept) |
0.3214 |
0.348 |
0.9237 |
0.3638 |
|
| om |
-0.1565 |
0.3904 |
-0.4009 |
0.6916 |
|
| gravel |
1.722 |
1.85 |
0.9311 |
0.36 |
|
| sand |
-0.2678 |
0.265 |
-1.011 |
0.3212 |
|
| silt |
0.02221 |
0.2747 |
0.08088 |
0.9361 |
|
| arsenic |
-0.5028 |
0.4029 |
-1.248 |
0.2228 |
|
| chromium |
-0.9681 |
0.4327 |
-2.237 |
0.03371 |
* |
| copper |
0.1013 |
0.484 |
0.2092 |
0.8359 |
|
| iron |
0.3639 |
0.2222 |
1.638 |
0.1131 |
|
| mercury |
0.3504 |
0.7251 |
0.4832 |
0.6329 |
|
| lead |
0.7228 |
0.9191 |
0.7864 |
0.4385 |
|
## RMSE from cross-validation: 1.326798
Variance Inflation Factors
| VIF |
2.43 |
1.47 |
1.66 |
1.7 |
2.47 |
2.69 |
2.97 |
1.39 |
2.05 |
5.61 |

## REDUCED MODEL
## Adjusted R2 is: 0.17
Fitting linear model: H ~ gravel + sand + arsenic + chromium + iron + lead
| (Intercept) |
0.3127 |
0.2695 |
1.16 |
0.2548 |
|
| gravel |
1.941 |
1.427 |
1.36 |
0.1835 |
|
| sand |
-0.2974 |
0.1863 |
-1.596 |
0.1206 |
|
| arsenic |
-0.4728 |
0.3203 |
-1.476 |
0.15 |
|
| chromium |
-0.9665 |
0.2956 |
-3.27 |
0.002635 |
* * |
| iron |
0.3668 |
0.1979 |
1.854 |
0.07331 |
|
| lead |
0.7646 |
0.4328 |
1.767 |
0.08711 |
|
## RMSE from cross-validation: 1.066136
Variance Inflation Factors
| VIF |
1.21 |
1.24 |
2.09 |
1.96 |
1.32 |
2.81 |

Evenness
## FULL MODEL
## Adjusted R2 is: 0.18
Fitting linear model: J ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
| (Intercept) |
0.2272 |
0.3204 |
0.709 |
0.4844 |
|
| om |
-0.1924 |
0.3595 |
-0.5352 |
0.5969 |
|
| gravel |
1.286 |
1.703 |
0.755 |
0.4568 |
|
| sand |
-0.2388 |
0.244 |
-0.9784 |
0.3366 |
|
| silt |
0.08012 |
0.2529 |
0.3168 |
0.7538 |
|
| arsenic |
-0.4975 |
0.3711 |
-1.341 |
0.1912 |
|
| chromium |
-0.4977 |
0.3985 |
-1.249 |
0.2223 |
|
| copper |
0.05134 |
0.4457 |
0.1152 |
0.9092 |
|
| iron |
0.2896 |
0.2046 |
1.415 |
0.1684 |
|
| mercury |
0.1629 |
0.6678 |
0.2439 |
0.8092 |
|
| lead |
0.2292 |
0.8464 |
0.2708 |
0.7886 |
|
## RMSE from cross-validation: 1.002764
Variance Inflation Factors
| VIF |
2.43 |
1.47 |
1.66 |
1.7 |
2.47 |
2.69 |
2.97 |
1.39 |
2.05 |
5.61 |

## REDUCED MODEL
## Adjusted R2 is: 0.28
Fitting linear model: J ~ sand + arsenic + chromium + iron
| (Intercept) |
0.00684 |
0.1395 |
0.04904 |
0.9612 |
|
| sand |
-0.224 |
0.1677 |
-1.336 |
0.1907 |
|
| arsenic |
-0.4449 |
0.1602 |
-2.778 |
0.008952 |
* * |
| chromium |
-0.358 |
0.1934 |
-1.851 |
0.0732 |
|
| iron |
0.2699 |
0.1701 |
1.587 |
0.1221 |
|
## RMSE from cross-validation: 0.9386957
Variance Inflation Factors
| VIF |
1.21 |
1.14 |
1.4 |
1.23 |

Parameters
Richness
## FULL MODEL
## Adjusted R2 is: 0.34
Fitting linear model: S ~ om + gravel + sand + silt
| (Intercept) |
0.02794 |
0.2434 |
0.1148 |
0.9093 |
|
| om |
0.3383 |
0.1531 |
2.209 |
0.03421 |
* |
| gravel |
0.175 |
1.284 |
0.1363 |
0.8924 |
|
| sand |
-0.1469 |
0.1649 |
-0.8906 |
0.3796 |
|
| silt |
-0.4431 |
0.185 |
-2.395 |
0.02246 |
* |
## RMSE from cross-validation: 0.9300972
Variance Inflation Factors
| VIF |
1.16 |
1.24 |
1.25 |
1.39 |

## REDUCED MODEL
## Adjusted R2 is: 0.36
Fitting linear model: S ~ om + silt
| (Intercept) |
-0.004759 |
0.1309 |
-0.03635 |
0.9712 |
|
| om |
0.3875 |
0.1407 |
2.753 |
0.009287 |
* * |
| silt |
-0.3649 |
0.1414 |
-2.582 |
0.01418 |
* |
## RMSE from cross-validation: 0.8989986
Variance Inflation Factors
| VIF |
1.08 |
1.08 |

Density
## FULL MODEL
## Adjusted R2 is: 0.54
Fitting linear model: N ~ om + gravel + sand + silt
| (Intercept) |
-0.0727 |
0.2013 |
-0.3611 |
0.7203 |
|
| om |
0.4731 |
0.1266 |
3.736 |
0.0007077 |
* * * |
| gravel |
-0.5186 |
1.062 |
-0.4885 |
0.6285 |
|
| sand |
-0.2515 |
0.1364 |
-1.844 |
0.07419 |
|
| silt |
-0.342 |
0.153 |
-2.235 |
0.03231 |
* |
## RMSE from cross-validation: 0.91835
Variance Inflation Factors
| VIF |
1.16 |
1.24 |
1.25 |
1.39 |

## REDUCED MODEL
## Adjusted R2 is: 0.55
Fitting linear model: N ~ om + sand + silt
| (Intercept) |
0.009598 |
0.1089 |
0.08811 |
0.9303 |
|
| om |
0.475 |
0.1252 |
3.795 |
0.0005805 |
* * * |
| sand |
-0.2793 |
0.1225 |
-2.279 |
0.02906 |
* |
| silt |
-0.3791 |
0.1313 |
-2.888 |
0.006695 |
* * |
## RMSE from cross-validation: 0.9212626
Variance Inflation Factors
| VIF |
1.16 |
1.14 |
1.21 |

Diversity
## FULL MODEL
## Adjusted R2 is: -0.09
Fitting linear model: H ~ om + gravel + sand + silt
| (Intercept) |
0.2396 |
0.3172 |
0.7553 |
0.4554 |
|
| om |
-0.04775 |
0.1995 |
-0.2393 |
0.8123 |
|
| gravel |
1.383 |
1.673 |
0.8268 |
0.4143 |
|
| sand |
-0.133 |
0.2149 |
-0.6188 |
0.5403 |
|
| silt |
-0.1739 |
0.2411 |
-0.7212 |
0.4759 |
|
## RMSE from cross-validation: 1.237837
Variance Inflation Factors
| VIF |
1.16 |
1.24 |
1.25 |
1.39 |

## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: H ~ 1
| (Intercept) |
0.01659 |
0.1658 |
0.1001 |
0.9208 |
|
## RMSE from cross-validation: 1.027593
Quitting from lines 417-419 (C1_analyses_14B.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : There were 22 warnings (use warnings() to see them)
Evenness
## FULL MODEL
## Adjusted R2 is: 0.05
Fitting linear model: J ~ om + gravel + sand + silt
| (Intercept) |
0.2087 |
0.2928 |
0.713 |
0.4809 |
|
| om |
-0.247 |
0.1842 |
-1.341 |
0.189 |
|
| gravel |
1.189 |
1.544 |
0.7698 |
0.4469 |
|
| sand |
-0.07851 |
0.1983 |
-0.3958 |
0.6948 |
|
| silt |
0.105 |
0.2225 |
0.472 |
0.64 |
|
## RMSE from cross-validation: 1.056183
Variance Inflation Factors
| VIF |
1.16 |
1.24 |
1.25 |
1.39 |

## REDUCED MODEL
## Adjusted R2 is: 0.08
Fitting linear model: J ~ om
| (Intercept) |
0.0259 |
0.1575 |
0.1644 |
0.8703 |
|
| om |
-0.3206 |
0.1567 |
-2.046 |
0.04813 |
* |
## RMSE from cross-validation: 0.986031
Variance Inflation Factors
| VIF |
1 |

5. Multivariate regression
Independant variables are habitat parameters and heavy metal concentrations, dependant variables are species abundances. Variables have been standardized by mean and standard-deviation, and outliers and correlated variables have been excluded. Taxon densities were (log+1) transformed.
This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.4.
⏪ | 📄